Weighted Centroid Distance
A new model assessment technique for multi-class classification tasks in machine learning.
For the statistical theory, please see the white paper in /paper. It is auto
generated from .tex to .pdf.
How to use
# Python
from weighted_centroid_distance import WeightedCentroidDistance
distribution = WeightedCentroidDistance.get_distribution([1, 2, 3, 3, 4, 5, 5, 5], inverse=False)
wcd = WeightedCentroidDistance(distribution=distribution)
y____ = [1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5]
y_hat = [1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 1, 5, 5, 2]
res = wcd.get_distance(y=y____, y_hat=y_hat)
print("res: ", res)
# 0.12086457016125143
Todo
- Create graphs for all parts of the algorithm defined in the paper
- Improve python script, and make a versions for other languages.
1 maintainer
Metadata
1 maintainer
Release files for weighted-centroid-distance 1.0.0
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| weighted_centroid_distance-1.0.0-py3-none-any.whl | Python 3 | none | any | Details for weighted_centroid_distance-1.0.0-py3-none-any.whl |
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Release files / weighted_centroid_distance-1.0.0.tar.gz
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Release files / weighted_centroid_distance-1.0.0-py3-none-any.whl
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